This book provides a state-of-the-art overview of the combined use of imaging modalities to obtain important functional and morphological information on intravascular disease and enhance disease detection. It discusses the integration of intravascular ultrasound (IVUS, intravascular optical coherence tomography (OCT), intravascular photoacoustic imaging (IVPA) and acoustic radiation force optical coherence elastography (ARF-OCE), and introduces the integration of multimodality imaging systems, such as IR and florescence. It includes the latest research advances and numerous imaging photos to offer readers insights into current intravascular applications. It is a valuable resource for students, scientists and physicians wanting to gain a deeper understanding of multimodality imaging tools.

This book presents different approaches on multi-modality imaging with a focus on biomedical applications. Medical imaging can be divided into two categories: functional (related to physiological body measurements) and anatomical (structural) imaging modalities. In particular, this book covers imaging combinations coming from the usual popular modalities (such as the anatomical modalities, e.g. X-ray, CT and MRI), and it also includes some promising and new imaging modalities that are still being developed and improved (such as infrared thermography (IRT) and photoplethysmography imaging (PPGI)), implying potential approaches for innovative biomedical applications. Moreover, this book includes a variety of tools on computer vision, imaging processing, and computer graphics, which led to the generation and visualization of 3D models, making the most recent advances in this area possible. This is an ideal book for students and biomedical engineering researchers covering the biomedical imaging field.

The imaging of moving organs such as the heart, in particular, is a real challenge because of its movement. This book presents current and emerging methods developed for the acquisition of images of moving organs in the five main medical imaging modalities: conventional X-rays, computed tomography (CT), magnetic resonance imaging (MRI), nuclear imaging and ultrasound. The availability of dynamic image sequences allows for the qualitative and quantitative assessment of an organ’s dynamics, which is often linked to pathologies.

Written by an interdisciplinary team of experts, Cardiac Imaging: A Multimodality Approach features an in-depth introduction to all current imaging modalities for the diagnostic assessment of the heart as well as a clinical overview of cardiac diseases and main indications for cardiac imaging. With a particular emphasis on CT and MRI, the first part of the atlas also covers conventional radiography, echocardiography, angiography and nuclear medicine imaging. Leading specialists demonstrate the latest advances in the field, and compare the strengths and weaknesses of each modality. The book's second part features clinical chapters on heart defects, endocarditis, coronary heart disease, cardiomyopathies, myocarditis, cardiac tumors, pericardial diseases, pulmonary vascular diseases, and diseases of the thoracic aorta. The authors address anatomy, pathophysiology, and clinical features, and evaluate the various diagnostic options.Key features: Highly regarded experts in cardiology and radiology offer image-based teaching of the latest techniques Readers learn how to decide which modality to use for which indication Visually highlighted tables and essential points allow for easy navigation through the text More than 600 outstanding images show up-to-date technology and current imaging protocols Cardiac Imaging: A Multimodality Approach is a must-have desk reference for cardiologists and radiologists in practice, as well as a study guide for residents in both fields. It will also appeal to cardiac surgeons, general practitioners, and medical physicists with a special interest in imaging of the heart.

This book provides a practically applicable guide to the use of multimodality imaging for managing aortic valve disorders. Details of how to successfully diagnose a range of aortic valve diseases is presented, with detailed information covered on the techniques and relevant criteria for pre-operative screening, surgical and transcatheter planning, intraprocedural imaging and related postoperative follow-up. Multimodality Imaging for Cardiac Valvular Interventions, Volume 1 Aortic Valve: From Diagnosis to Decision-Making enables the reader to develop a thorough understanding and confidence of how to make appropriate clinical decisions when confronted by a range of aortic valve diseases. It is therefore a key resource for practicing cardiac imagers, cardiologists and interventional cardiologists who encounter these patients in their day-to-day clinical practice and trainees in cardiology seeking insight on the appropriate management of these patients.

There is an urgent need to develop and integrate new statistical, mathematical, visualization, and computational models with the ability to analyze Big Data in order to retrieve useful information to aid clinicians in accurately diagnosing and treating patients. The main focus of this book is to review and summarize state-of-the-art big data and deep learning approaches to analyze and integrate multiple data types for the creation of a decision matrix to aid clinicians in the early diagnosis and identification of high risk patients for human diseases and disorders. Leading researchers will contribute original research book chapters analyzing efforts to solve these important problems.

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.